In general, “adaptation” is a process of modifying certain parameters of a previously created (i.e., trained) system using a new set of observation data (“adaptation data”) which represent a sample of a class (or classes) known to the system but taken from a presumably different environment, i.e., exhibiting slightly different behavior, as compared to the samples of the same class that were used in the original system training.
Standard adaptation techniques modify the system's “structural” parameters, for example the statistical mean and covariance values (in systems with Gaussian density models), so as to maximize some objective function, e.g., the observation probability or likelihood of the adaptation data, whereby these structural parameters are the same as those estimated in the primary system training. Due to the fact that the number of such parameters may be high in complex systems, an effective adaptation requires a correspondingly large amount of adaptation data in order to achieve robustness of the modified parameters. In view of this, a need has been recognized in connection with undertaking adaptation with smaller amounts of data.